2025
SuperGLUE facilitates an explainable training framework for multi-modal data analysis
Liu T, Zhao J, Zhao H. SuperGLUE facilitates an explainable training framework for multi-modal data analysis. Cell Reports Methods 2025, 5: 101167. PMID: 40914154, DOI: 10.1016/j.crmeth.2025.101167.Peer-Reviewed Original ResearchConceptsData integrationProbabilistic deep learningMulti-modal data analysisInference of gene regulatory networksMulti-modal data integrationDeep learningGene regulatory networksTraining frameworkBaseline modelRegulatory networksComplex biological systemsRegulatory relationshipsSensing dataCell statesGlobal structureArea of active researchActive researchOmicsBiological featuresScalable methodFrameworkBiological systemsStatistical modelNetworkBiological linkages
2017
Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data
Wang Y, Fang H, Yang D, Zhao H, Deng M. Network Clustering Analysis Using Mixture Exponential-Family Random Graph Models and Its Application in Genetic Interaction Data. IEEE Transactions On Computational Biology And Bioinformatics 2017, 16: 1743-1752. PMID: 28858811, DOI: 10.1109/tcbb.2017.2743711.Peer-Reviewed Original ResearchConceptsExponential-family random graph modelsRandom graph modelsGraph modelStatistical network modelsHeterogeneity of networksLarge-scale genetic interaction networksReal social networksERGM parametersSubset of nodesOnline graphStatistical modelData sizeObserved networkEM algorithmNetwork informationGraph nodesMixture problemSocial networksFlexible wayNetwork modelNetwork clustersClassical methodsIncredible setInteraction dataNetwork
2003
Chromosome Maps
Speed T, Zhao H. Chromosome Maps. 2003 DOI: 10.1002/0470022620.bbc01.Peer-Reviewed Original Research
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply